# source: https://raw.githubusercontent.com/behradhesami/Projects/5ac4ca3ce403e5ccc9db229f5b32846bf2767a7d/Freqtrade/IchiV1/ichiV1.py import logging from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import (CategoricalParameter, DecimalParameter, IntParameter) from pandas import DataFrame import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import pandas as pd # noqa pd.options.mode.chained_assignment = None # default='warn' import technical.indicators as ftt from functools import reduce from datetime import datetime, timedelta from freqtrade.strategy import merge_informative_pair from freqtrade.strategy import stoploss_from_open from typing import Dict, Optional, Union logger = logging.getLogger(__name__) class Github_behradhesami_Projects__ichiV1__20240626_142549(IStrategy): INTERFACE_VERSION = 3 def version(self) -> str: return "v0.0.001" # Futures custom_leverage = 1.0 # NOTE: settings as of the 25th july 21 # Buy hyperspace params: #"buy_min_fan_magnitude_gain": 1.002 # NOTE: Good value (Win% ~70%), alot of trades #"buy_min_fan_magnitude_gain": 1.008 # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008, buy_trend_above_senkou_level = IntParameter(1,8, default=5, space="buy") buy_trend_bullish_level = IntParameter(1,8, default=6, space="buy") buy_fan_magnitude_shift_value = IntParameter(1,8, default=5, space="buy") buy_min_fan_magnitude_gain = DecimalParameter(0.980, 1.020, default=1.002, space="buy") buy_trend_above_senkou_level_short = IntParameter(1,8, default=5, space="buy") buy_trend_bullish_level_short = IntParameter(1,8, default=6, space="buy") buy_fan_magnitude_shift_value_short = IntParameter(1,8, default=5, space="buy") buy_min_fan_magnitude_gain_short = DecimalParameter(0.980, 1.020, default=1.002, space="buy") # Sell hyperspace params: # NOTE: was 15m but kept bailing out in dryrun sell_trend_indicator = CategoricalParameter(["trend_close_5m", "trend_close_15m", "trend_close_30m", "trend_close_1h", "trend_close_2h", "trend_close_4h", "trend_close_6h", "trend_close_8h"], default="trend_close_15m", space="sell") # ROI table: minimal_roi = { "0": 0.061 * custom_leverage, "3": 0.023 * custom_leverage, "6": 0.008 * custom_leverage, "19": 0 * custom_leverage, "300": -1.0 } # Stoploss: stoploss = -0.217 * custom_leverage # Optimal timeframe for the strategy timeframe = '1m' startup_candle_count = 130 #startup_candle_count = 96 #process_only_new_candles = True process_only_new_candles = False trailing_stop = False #trailing_stop_positive = 0.002 #trailing_stop_positive_offset = 0.025 #trailing_only_offset_is_reached = True can_short = True use_exit_signal = True exit_profit_only = False ignore_roi_if_entry_signal = False #calcola solo gli indicatori strettamente necessari se True optimize = False plot_config = { 'main_plot': { # fill area between senkou_a and senkou_b 'senkou_a': { 'color': 'green', #optional 'fill_to': 'senkou_b', 'fill_label': 'Ichimoku Cloud', #optional 'fill_color': 'rgba(255,76,46,0.2)', #optional }, # plot senkou_b, too. Not only the area to it. 'senkou_b': {}, 'trend_close_5m': {'color': '#FF5733'}, 'trend_close_15m': {'color': '#FF8333'}, 'trend_close_30m': {'color': '#FFB533'}, 'trend_close_1h': {'color': '#FFE633'}, 'trend_close_2h': {'color': '#E3FF33'}, 'trend_close_4h': {'color': '#C4FF33'}, 'trend_close_6h': {'color': '#61FF33'}, 'trend_close_8h': {'color': '#33FF7D'} }, 'subplots': { 'fan_magnitude': { 'fan_magnitude': {} }, 'fan_magnitude_gain': { 'fan_magnitude_gain': {} } } } def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: logger.info(f"START populate_indicators : {metadata['pair']}") heikinashi = qtpylib.heikinashi(dataframe) dataframe_open = heikinashi['open'] #dataframe_open = dataframe['open'] #dataframe_close = heikinashi['close'] dataframe_close = dataframe['close'] #dataframe['hk_close'] = heikinashi['close'] #dataframe['hk_high'] = heikinashi['high'] #dataframe['hk_low'] = heikinashi['low'] moltiplicatore = 1 #dipende dal timeframe if moltiplicatore > 1: dataframe['trend_close_5m'] = ta.EMA(dataframe_close, timeperiod=1*moltiplicatore) else: dataframe['trend_close_5m'] = dataframe_close if not self.optimize or (self.buy_trend_above_senkou_level.value >= 2 or self.buy_trend_bullish_level.value >= 2): dataframe['trend_close_15m'] = ta.EMA(dataframe_close, timeperiod=3*moltiplicatore) if not self.optimize or (self.buy_trend_above_senkou_level.value >= 3 or self.buy_trend_bullish_level.value >= 3): dataframe['trend_close_30m'] = ta.EMA(dataframe_close, timeperiod=6*moltiplicatore) #if not self.optimize or (self.buy_trend_above_senkou_level.value >= 4 or self.buy_trend_bullish_level.value >= 4): dataframe['trend_close_1h'] = ta.EMA(dataframe_close, timeperiod=12*moltiplicatore) #if not self.optimize or (self.buy_trend_above_senkou_level.value >= 5 or self.buy_trend_bullish_level.value >= 5): dataframe['trend_close_2h'] = ta.EMA(dataframe_close, timeperiod=24*moltiplicatore) if not self.optimize or (self.buy_trend_above_senkou_level.value >= 6 or self.buy_trend_bullish_level.value >= 6): dataframe['trend_close_4h'] = ta.EMA(dataframe_close, timeperiod=48*moltiplicatore) if not self.optimize or (self.buy_trend_above_senkou_level.value >= 7 or self.buy_trend_bullish_level.value >= 7): dataframe['trend_close_6h'] = ta.EMA(dataframe_close, timeperiod=72*moltiplicatore) #if not self.optimize or (self.buy_trend_above_senkou_level.value >= 8 or self.buy_trend_bullish_level.value >= 8): dataframe['trend_close_8h'] = ta.EMA(dataframe_close, timeperiod=96*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 1): if moltiplicatore > 1: dataframe['trend_open_5m'] = ta.EMA(dataframe_open, timepriod=1*moltiplicatore) else: dataframe['trend_open_5m'] = dataframe_open if not self.optimize or (self.buy_trend_bullish_level.value >= 2): dataframe['trend_open_15m'] = ta.EMA(dataframe_open, timeperiod=3*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 3): dataframe['trend_open_30m'] = ta.EMA(dataframe_open, timeperiod=6*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 4): dataframe['trend_open_1h'] = ta.EMA(dataframe_open, timeperiod=12*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 5): dataframe['trend_open_2h'] = ta.EMA(dataframe_open, timeperiod=24*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 6): dataframe['trend_open_4h'] = ta.EMA(dataframe_open, timeperiod=48*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 7): dataframe['trend_open_6h'] = ta.EMA(dataframe_open, timeperiod=72*moltiplicatore) if not self.optimize or (self.buy_trend_bullish_level.value >= 8): dataframe['trend_open_8h'] = ta.EMA(dataframe_open, timeperiod=96*moltiplicatore) dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h']) dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1) ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30) #dataframe['chikou_span'] = ichimoku['chikou_span'] NON UTILIZZARE: ha bias #dataframe['tenkan_sen'] = ichimoku['tenkan_sen'] #dataframe['kijun_sen'] = ichimoku['kijun_sen'] dataframe['senkou_a'] = ichimoku['senkou_span_a'] dataframe['senkou_b'] = ichimoku['senkou_span_b'] #dataframe['leading_senkou_span_a'] = ichimoku['leading_senkou_span_a'] #dataframe['leading_senkou_span_b'] = ichimoku['leading_senkou_span_b'] #dataframe['cloud_green'] = ichimoku['cloud_green'] #dataframe['cloud_red'] = ichimoku['cloud_red'] #dataframe['atr'] = ta.ATR(dataframe) logger.info(f"END populate_indicators : {metadata['pair']}") return dataframe def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] # Trending market if self.buy_trend_above_senkou_level.value >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_5m'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_15m'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_30m'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_1h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_2h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_4h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_6h'] > dataframe['senkou_b']) if self.buy_trend_above_senkou_level.value >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_a']) conditions_long.append(dataframe['trend_close_8h'] > dataframe['senkou_b']) # Trends bullish if self.buy_trend_bullish_level.value >= 1: conditions_long.append(dataframe['trend_close_5m'] > dataframe['trend_open_5m']) if self.buy_trend_bullish_level.value >= 2: conditions_long.append(dataframe['trend_close_15m'] > dataframe['trend_open_15m']) if self.buy_trend_bullish_level.value >= 3: conditions_long.append(dataframe['trend_close_30m'] > dataframe['trend_open_30m']) if self.buy_trend_bullish_level.value >= 4: conditions_long.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h']) if self.buy_trend_bullish_level.value >= 5: conditions_long.append(dataframe['trend_close_2h'] > dataframe['trend_open_2h']) if self.buy_trend_bullish_level.value >= 6: conditions_long.append(dataframe['trend_close_4h'] > dataframe['trend_open_4h']) if self.buy_trend_bullish_level.value >= 7: conditions_long.append(dataframe['trend_close_6h'] > dataframe['trend_open_6h']) if self.buy_trend_bullish_level.value >= 8: conditions_long.append(dataframe['trend_close_8h'] > dataframe['trend_open_8h']) # Trends magnitude conditions_long.append(dataframe['fan_magnitude_gain'] >= self.buy_min_fan_magnitude_gain.value) conditions_long.append(dataframe['fan_magnitude'] > 1) for x in range(self.buy_fan_magnitude_shift_value.value): conditions_long.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'enter_long'] = 1 ############################################### SHORT CONDITION ########################################################### conditions_short= [] if self.buy_trend_above_senkou_level_short.value <= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_5m'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_15m'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_30m'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_1h'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_2h'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_4h'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_6h'] < dataframe['senkou_b']) if self.buy_trend_above_senkou_level_short.value <= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_a']) conditions_short.append(dataframe['trend_close_8h'] < dataframe['senkou_b']) # Trends bullish if self.buy_trend_bullish_level_short.value <= 1: conditions_short.append(dataframe['trend_close_5m'] < dataframe['trend_open_5m']) if self.buy_trend_bullish_level_short.value <= 2: conditions_short.append(dataframe['trend_close_15m'] < dataframe['trend_open_15m']) if self.buy_trend_bullish_level_short.value <= 3: conditions_short.append(dataframe['trend_close_30m'] < dataframe['trend_open_30m']) if self.buy_trend_bullish_level_short.value <= 4: conditions_short.append(dataframe['trend_close_1h'] < dataframe['trend_open_1h']) if self.buy_trend_bullish_level_short.value <= 5: conditions_short.append(dataframe['trend_close_2h'] < dataframe['trend_open_2h']) if self.buy_trend_bullish_level_short.value <= 6: conditions_short.append(dataframe['trend_close_4h'] < dataframe['trend_open_4h']) if self.buy_trend_bullish_level_short.value <= 7: conditions_short.append(dataframe['trend_close_6h'] < dataframe['trend_open_6h']) if self.buy_trend_bullish_level_short.value <= 8: conditions_short.append(dataframe['trend_close_8h'] < dataframe['trend_open_8h']) # Trends magnitude conditions_short.append(dataframe['fan_magnitude_gain'] <= self.buy_min_fan_magnitude_gain_short.value) conditions_short.append(dataframe['fan_magnitude'] < 1) for x in range(self.buy_fan_magnitude_shift_value_short.value): conditions_short.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude']) if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions_long = [] conditions_long.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_trend_indicator.value])) if conditions_long: dataframe.loc[ reduce(lambda x, y: x & y, conditions_long), 'exit_long'] = 1 conditions_short = [] conditions_short.append(qtpylib.crossed_above(dataframe['trend_close_5m'], dataframe[self.sell_trend_indicator.value])) if conditions_short: dataframe.loc[ reduce(lambda x, y: x & y, conditions_short), 'exit_short'] = 1 return dataframe def leverage(self, pair: str, current_time: datetime, current_rate: float, proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str, **kwargs) -> float: """ Customize leverage for each new trade. This method is only called in futures mode. :param pair: Pair that's currently analyzed :param current_time: datetime object, containing the current datetime :param current_rate: Rate, calculated based on pricing settings in exit_pricing. :param proposed_leverage: A leverage proposed by the bot. :param max_leverage: Max leverage allowed on this pair :param entry_tag: Optional entry_tag (buy_tag) if provided with the buy signal. :param side: 'long' or 'short' - indicating the direction of the proposed trade :return: A leverage amount, which is between 1.0 and max_leverage. """ return self.custom_leverage